Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.

中文翻译：

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基于智能马尔可夫最优决策过程的自动首到拣选方法

选取初至是地震处理中的一个重要步骤。大量的地震数据需要自动和客观的拾取。在本文中，我们将首到拣选制定为多维特征属性空间中的智能马尔可夫决策过程。通过设计合理的模型，在奖励函数空间进行全局优化，得到累积奖励值最大的路径，达到自动拾取先到的目的。状态值函数包含与距离相关的折扣因子 γ，它使马尔可夫决策过程能够拾取初至连续性以考虑地震数据的横向连续性并避免地震数据中的不良道信息。在此基础上，本文方法进一步引入了基于模糊聚类的多维属性奖励函数和基于结构的高斯随机策略的优化模型，从而降低了模型设计的难度，使地震数据的拾取更加准确和自动化。 . 在现场地震数据中测试该方法，揭示了其特性，表明它可以自动拾取更合理的初至并具有一定的质量控制能力，特别是初至能量较弱（信噪比低）或浅层有相邻的复杂波形。使地震数据采集更准确、更自动化。在现场地震数据中测试该方法，揭示了其特性，表明它可以自动拾取更合理的初至并具有一定的质量控制能力，特别是初至能量较弱（信噪比低）或浅层有相邻的复杂波形。使地震数据采集更准确、更自动化。在现场地震数据中测试该方法，揭示了其特性，表明它可以自动拾取更合理的初至并具有一定的质量控制能力，特别是初至能量较弱（信噪比低）或浅层有相邻的复杂波形。